By Md Salek Miah — Statistician & ML Researcher | SUST, Bangladesh | saleksta@gmail.com
Teaching statistics for ML to researchers, epidemiologists, and data scientists — from first principles to advanced methods.
🔵 Part 1 — Foundations of Statistics (Posts 1–20)
| # | Topic | Link |
|---|
| 1 | Types of Data (Nominal, Ordinal, Interval, Ratio) | Read → |
| 2 | Measures of Central Tendency | Read → |
| 3 | Measures of Dispersion | Read → |
| 4 | Skewness & Kurtosis | Read → |
| 5 | Covariance & Correlation | Read → |
| 6 | Probability Axioms & Rules | Read → |
| 7 | Conditional Probability | Read → |
| 8 | Bayes’ Theorem | Read → |
| 9 | Random Variables | Read → |
| 10 | Probability Mass Function (PMF) | Read → |
| 11 | Probability Density Function (PDF) | Read → |
| 12 | Cumulative Distribution Function (CDF) | Read → |
| 13 | Joint, Marginal & Conditional Distributions | Read → |
| 14 | Expected Value & Variance | Read → |
| 15 | Law of Large Numbers | Read → |
| 16 | Central Limit Theorem (CLT) | Read → |
| 17 | Sampling & Sampling Distributions | Read → |
| 18 | Standard Error | Read → |
| 19 | Degrees of Freedom | Read → |
| 20 | Moments of a Distribution | Read → |
🟢 Part 2 — Probability Distributions (Posts 21–35)
| # | Topic | Link |
|---|
| 21 | Bernoulli Distribution | Read → |
| 22 | Binomial Distribution | Read → |
| 23 | Poisson Distribution | Read → |
| 24 | Geometric Distribution | Read → |
| 25 | Uniform Distribution | Read → |
| 26 | Normal (Gaussian) Distribution | Read → |
| 27 | Standard Normal & Z-scores | Read → |
| 28 | Student’s t-Distribution | Read → |
| 29 | Chi-Square Distribution | Read → |
| 30 | F-Distribution | Read → |
| 31 | Exponential Distribution | Read → |
| 32 | Beta Distribution | Read → |
| 33 | Dirichlet Distribution | Read → |
| 34 | Multivariate Normal Distribution | Read → |
| 35 | Log-Normal Distribution | Read → |
🟡 Part 3 — Statistical Inference (Posts 36–50)
| # | Topic | Link |
|---|
| 36 | Point Estimation | Read → |
| 37 | Confidence Intervals | Read → |
| 38 | Properties of Estimators (Bias, Variance, Consistency) | Read → |
| 39 | Maximum Likelihood Estimation (MLE) | Read → |
| 40 | Method of Moments | Read → |
| 41 | Bayesian Estimation & Posterior Distribution | Read → |
| 42 | Conjugate Priors | Read → |
| 43 | Hypothesis Testing Framework | Read → |
| 44 | Type I & Type II Errors | Read → |
| 45 | p-value & Statistical Significance | Read → |
| 46 | z-test & t-test | Read → |
| 47 | Chi-Square Test | Read → |
| 48 | ANOVA | Read → |
| 49 | Non-parametric Tests | Read → |
| 50 | Multiple Testing & Bonferroni Correction | Read → |
🟠 Part 4 — Regression & Prediction (Posts 51–63)
| # | Topic | Link |
|---|
| 51 | Simple Linear Regression | Read → |
| 52 | Multiple Linear Regression | Read → |
| 53 | OLS Estimation | Read → |
| 54 | Gauss-Markov Theorem & BLUE | Read → |
| 55 | R² & Adjusted R² | Read → |
| 56 | Residual Analysis & Diagnostics | Read → |
| 57 | Multicollinearity & VIF | Read → |
| 58 | Heteroscedasticity & WLS | Read → |
| 59 | Autocorrelation & Durbin-Watson | Read → |
| 60 | Logistic Regression & Log-Odds | Read → |
| 61 | Poisson Regression | Read → |
| 62 | Ridge, Lasso & Elastic Net | Read → |
| 63 | Polynomial & Nonlinear Regression | Read → |
🔴 Part 5 — ML-Specific Statistical Concepts (Posts 64–78)
| # | Topic | Link |
|---|
| 64 | Bias-Variance Tradeoff | Read → |
| 65 | Overfitting & Underfitting | Read → |
| 66 | Train/Validation/Test Split | Read → |
| 67 | Cross-Validation (k-Fold, LOOCV) | Read → |
| 68 | Bootstrap & Bagging | Read → |
| 69 | Feature Selection Methods | Read → |
| 70 | PCA | Read → |
| 71 | SVD | Read → |
| 72 | Factor Analysis | Read → |
| 73 | Entropy & Information Gain | Read → |
| 74 | Gini Impurity | Read → |
| 75 | ROC Curve & AUC | Read → |
| 76 | Precision, Recall, F1-Score | Read → |
| 77 | Calibration & Probability Scoring | Read → |
| 78 | Imbalanced Data — SMOTE, Class Weights | Read → |
🟣 Part 6 — Bayesian & Probabilistic ML (Posts 79–87)
⚫ Part 7 — Deep Learning Foundations (Posts 88–96)
| # | Topic | Link |
|---|
| 88 | Loss Functions | Read → |
| 89 | Gradient Descent & Variants | Read → |
| 90 | Backpropagation & Chain Rule | Read → |
| 91 | Activation Functions | Read → |
| 92 | Batch Normalization | Read → |
| 93 | Dropout as Regularization | Read → |
| 94 | Weight Initialization | Read → |
| 95 | Vanishing & Exploding Gradients | Read → |
| 96 | Autoencoders & VAE | Read → |
⏺️ Part 8 — Advanced & Applied (Posts 97–100)
| # | Topic | Link |
|---|
| 97 | Time Series (ARIMA, ACF, PACF) | Read → |
| 98 | Survival Analysis & Hazard Functions | Read → |
| 99 | Causal Inference (DAGs, Do-Calculus) | Read → |
| 100 | A/B Testing & Experimentation Design | Read → |
About the Author
Md Salek Miah is a statistician and machine learning researcher at SUST, Bangladesh, with 2 published and 20+ manuscripts under review in Q1 journals. His research applies advanced ML, explainable AI (SHAP/LIME), and spatial analysis to maternal health, child health, and mental health outcomes across South Asia and Sub-Saharan Africa using DHS survey data.
